{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# 基于位置的前馈网络\n",
    "class PositionWiseFFN(nn.Module):\n",
    "    def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs, **kwargs):\n",
    "        super(PositionWiseFFN, self).__init__(**kwargs)\n",
    "        self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor([[ 0.0629, -0.2823,  0.4656,  0.1501,  0.5358,  0.2567,  0.6316,  0.2184],\n        [ 0.0629, -0.2823,  0.4656,  0.1501,  0.5358,  0.2567,  0.6316,  0.2184],\n        [ 0.0629, -0.2823,  0.4656,  0.1501,  0.5358,  0.2567,  0.6316,  0.2184]],\n       grad_fn=<SelectBackward0>)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 改变张量的最里层维度的尺寸\n",
    "ffn = PositionWiseFFN(4, 4, 8)\n",
    "ffn.eval()\n",
    "ffn(torch.ones((2, 3, 4)))[0]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer norm: tensor([[-1.0000,  1.0000],\n",
      "        [-1.0000,  1.0000]], grad_fn=<NativeLayerNormBackward0>) \n",
      "batch norm: tensor([[-1.0000, -1.0000],\n",
      "        [ 1.0000,  1.0000]], grad_fn=<NativeBatchNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# 对比不同维度的层归一化和批量归一化的效果\n",
    "ln = nn.LayerNorm(2)\n",
    "bn = nn.BatchNorm1d(2)\n",
    "X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)\n",
    "print('layer norm:', ln(X), '\\nbatch norm:', bn(X))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# 使用残差连接和层归一化\n",
    "class AddNorm(nn.Module):\n",
    "    def __init__(self, normalized_shape, dropout, **kwargs):\n",
    "        super(AddNorm, self).__init__(**kwargs)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(normalized_shape)\n",
    "\n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "torch.Size([2, 3, 4])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加法操作后输出张量的形状相同\n",
    "add_norm = AddNorm([3, 4], 0.5)\n",
    "add_norm.eval()\n",
    "add_norm(torch.ones((2, 3, 4)), torch.ones((2, 3, 4))).shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# 实现编码器中的一个层\n",
    "class EncoderBlock(nn.Module):\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, use_bias=False, **kwargs):\n",
    "        super(EncoderBlock, self).__init__(**kwargs)\n",
    "        self.attention = d2l.MultiHeadAttention(key_size, query_size,\n",
    "                                                value_size, num_hiddens,\n",
    "                                                num_heads, dropout, use_bias)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "torch.Size([2, 100, 24])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformer编码器中的任何层都不会改变其输入的状态\n",
    "X = torch.ones((2, 100, 24))\n",
    "valid_lens = torch.tensor([3, 2])\n",
    "encoder_blk = EncoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5)\n",
    "encoder_blk.eval()\n",
    "encoder_blk(X, valid_lens).shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# Transformer编码器\n",
    "class TransformerEncoder(d2l.Encoder):\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, use_bias=False, **kwargs):\n",
    "        super(TransformerEncoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\n",
    "                \"block\" + str(i),\n",
    "                EncoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, use_bias))\n",
    "\n",
    "    def forward(self, X, valid_lens, *args):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "torch.Size([2, 100, 24])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个两层的Transformer编码器\n",
    "encoder = TransformerEncoder(200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)\n",
    "encoder.eval()\n",
    "encoder(torch.ones((2, 100), dtype=torch.long), valid_lens).shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# Transformer解码器也是由多个相同的层组成\n",
    "class DecoderBlock(nn.Module):\n",
    "    \"\"\"解码器中第 i 个块\"\"\"\n",
    "\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, i, **kwargs):\n",
    "        super(DecoderBlock, self).__init__(**kwargs)\n",
    "        self.i = i\n",
    "        self.attention1 = d2l.MultiHeadAttention(key_size, query_size,\n",
    "                                                 value_size, num_hiddens,\n",
    "                                                 num_heads, dropout)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.attention2 = d2l.MultiHeadAttention(key_size, query_size,\n",
    "                                                 value_size, num_hiddens,\n",
    "                                                 num_heads, dropout)\n",
    "\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm3 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), axis=1)\n",
    "        state[2][self.i] = key_values\n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            dec_valid_lens = torch.arange(1, num_steps + 1,\n",
    "                                          device=X.device).repeat(batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "torch.Size([2, 100, 24])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 编码器和解码器的特征维度都是num_hiddens\n",
    "decoder_blk = DecoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5, 0)\n",
    "decoder_blk.eval()\n",
    "X = torch.ones((2, 100, 24))\n",
    "state = [encoder_blk(X, valid_lens), valid_lens, [None]]\n",
    "decoder_blk(X, state)[0].shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# Transform解码器\n",
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, **kwargs):\n",
    "        super(TransformerDecoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_layers = num_layers\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\n",
    "                \"block\" + str(i),\n",
    "                DecoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, i))\n",
    "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_layers]\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights = [[None] * len(self.blks) for _ in range(2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            self._attention_weights[0][i] = blk.attention1.attention.attention_weights\n",
    "            self._attention_weights[1][i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "def read_data_nmt():\n",
    "    \"\"\"载入 “英语-法语” 数据集 \"\"\"\n",
    "    data_dir = d2l.download_extract('fra-eng')\n",
    "    with open(os.path.join(data_dir, 'fra.txt'), 'r', encoding='utf-8') as f:\n",
    "        return f.read()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "def preprocess_nmt(text):\n",
    "    \"\"\"预处理 “英语-法语” 数据集\"\"\"\n",
    "\n",
    "    def no_space(char, prev_char):\n",
    "        return char in set(',.!?') and prev_char != ''\n",
    "\n",
    "    text = text.replace('\\u202f', ' ').replace('\\xa0', ' ').lower()\n",
    "    out = [\n",
    "        ' ' + char if i > 0 and no_space(char, text[i - 1]) else char\n",
    "        for i, char in enumerate(text)]\n",
    "    return ''.join(out)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "def tokenize_nmt(text, num_examples=None):\n",
    "    \"\"\"词元化 “英语-法语” 数据数据集 \"\"\"\n",
    "    source, target = [], []\n",
    "    for i, line in enumerate(text.split('\\n')):\n",
    "        if num_examples and i > num_examples:\n",
    "            break\n",
    "        parts = line.split('\\t')\n",
    "        if len(parts) == 2:\n",
    "            source.append(parts[0].split(' '))\n",
    "            target.append(parts[1].split(' '))\n",
    "    return source, target"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "def truncate_pad(line, num_steps, padding_token):\n",
    "    \"\"\"截断或填充文本序列\"\"\"\n",
    "    if len(line) > num_steps:\n",
    "        return line[:num_steps]\n",
    "    return line + [padding_token] * (num_steps - len(line))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "def build_array_nmt(lines, vocab, num_steps):\n",
    "    \"\"\"将机器翻译的文本序列转换成小批量\"\"\"\n",
    "    lines = [vocab[l] for l in lines]\n",
    "    lines = [l + [vocab['<eos>']] for l in lines]\n",
    "    array = torch.tensor([truncate_pad(l, num_steps, vocab['<pad>']) for l in lines])\n",
    "    valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)\n",
    "    return array, valid_len"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "def load_data_nmt(batch_size, num_steps, num_examples=600):\n",
    "    \"\"\"返回翻译数据集的迭代器和词汇表\"\"\"\n",
    "    text = preprocess_nmt(read_data_nmt())\n",
    "    source, target = tokenize_nmt(text, num_examples)\n",
    "    src_vocab = d2l.Vocab(source, min_freq=2,\n",
    "                          reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
    "    tgt_vocab = d2l.Vocab(target, min_freq=2,\n",
    "                          reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
    "    src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)\n",
    "    tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)\n",
    "    data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)\n",
    "    data_iter = d2l.load_array(data_arrays, batch_size)\n",
    "    return data_iter, src_vocab, tgt_vocab"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.030, 10494.5 tokens/sec on cuda:0\n"
     ]
    },
    {
     "data": {
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   "source": [
    "# 训练\n",
    "num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10\n",
    "lr, num_epochs, device = 0.005, 200, d2l.try_gpu()\n",
    "ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4\n",
    "key_size, query_size, value_size = 32, 32, 32\n",
    "norm_shape = [32]\n",
    "train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size, num_steps)\n",
    "encoder = TransformerEncoder(len(src_vocab), key_size, query_size, value_size,\n",
    "                             num_hiddens, norm_shape, ffn_num_input,\n",
    "                             ffn_num_hiddens, num_heads, num_layers, dropout)\n",
    "decoder = TransformerDecoder(len(tgt_vocab), key_size, query_size, value_size,\n",
    "                             num_hiddens, norm_shape, ffn_num_input,\n",
    "                             ffn_num_hiddens, num_heads, num_layers, dropout)\n",
    "net = d2l.EncoderDecoder(encoder, decoder)\n",
    "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go . => va au lit  !,  bleu 0.000\n",
      "i lost . => j'ai perdu .,  bleu 1.000\n",
      "he's calm . => il est calme .,  bleu 1.000\n",
      "i'm home . => je suis chez moi .,  bleu 1.000\n"
     ]
    }
   ],
   "source": [
    "# 将一些英语句子翻译成法语\n",
    "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "for eng, fra in zip(engs, fras):\n",
    "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
    "        net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
    "    print(f'{eng} => {translation}, ',\n",
    "          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "torch.Size([2, 4, 10, 10])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可视化Transformer的注意力权重\n",
    "enc_attention_weights = torch.cat(net.encoder.attention_weights, 0).reshape((\n",
    "    num_layers, num_heads, -1, num_steps))\n",
    "enc_attention_weights.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.show_heatmaps(enc_attention_weights.cpu(), xlabel='Key positions',\n",
    "                  ylabel='Query position',\n",
    "                  titles=['Head %d' % i\n",
    "                          for i in range(1, 5)], figsize=(7, 3.5))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "(torch.Size([2, 4, 6, 10]), torch.Size([2, 4, 6, 10]))"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为了可视化解码器的自注意力权重和 \"编码器-解码器\" 的注意力权重，我们需要完成更多的数据操作工作\n",
    "dec_attention_weights_2d = [\n",
    "    head[0].tolist() for step in dec_attention_weight_seq for attn in step\n",
    "    for blk in attn for head in blk]\n",
    "dec_attention_weights_filled = torch.tensor(\n",
    "    pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)\n",
    "dec_attention_weights = dec_attention_weights_filled.reshape(\n",
    "    (-1, 2, num_layers, num_heads, num_steps))\n",
    "dec_self_attention_weights, dec_inter_attention_weights = dec_attention_weights.permute(1, 2, 3, 0, 4)\n",
    "dec_self_attention_weights.shape, dec_inter_attention_weights.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.show_heatmaps(dec_self_attention_weights[:, :, :, :len(translation.split()) + 1],\n",
    "                  xlabel='Key positions', ylabel='Query position',\n",
    "                  titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "# 输出序列的查询不会与输入序列中填充位置的标记进行注意力计算\n",
    "d2l.show_heatmaps(dec_inter_attention_weights, xlabel='Key positions',\n",
    "                  ylabel='Query positions',\n",
    "                  titles=['Head %d' % i\n",
    "                          for i in range(1, 5)], figsize=(7, 3.5))"
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   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
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